EDITORIAL

Advances in artificial immune systems

Emma Hart • Chris McEwan • Jon Timmis •

Andrew Hone

Published online: 19 May 2011

� Springer-Verlag 2011

The field of Artificial Immune Systems (AIS) derives

inspiration from processes and mechanisms apparent in the

biological immune system. After early applications of this

paradigm to problems in anomaly detection and classifi-

cation, the field rapidly expanded, leading to a number of

successful optimization algorithms, and more recently,

applications in fields as diverse as swarm robotics and

wireless sensor networks.

In contrast to the situation in most other areas of bio-

logically-inspired computing, practitioners of AIS maintain

close links with the field that inspires their work. The field

of immunology itself is continually moving, with new

discoveries constantly refining the knowledge and under-

standing we have of the natural immune system. New

immunological knowledge is translated to AIS in the form

of new algorithms and architectures, but in addition,

research driven by computational modelling also informs

immunology. This two-way flow of knowledge between

engineering and immunology distinguishes the field from

other biological paradigms in computing, where the

underlying biological phenomena are perhaps more clearly

understood. In parallel to this, a body of literature has now

emerged which provides a theoretical underpinning for the

field, employing many of the same techniques that are used

to analyze other randomized search algorithms. This

facilitates a more rigorous comparison between AIS algo-

rithms and other search algorithms, and further enables

crossover of ideas between AIS and other paradigms.

The papers in this special issue—selected from papers

submitted following the 9th International Conference on

Artificial Immune Systems (ICARIS) held in Edinburgh in

July 2010—capture the essence of the current state of the

art in the field, illustrating the aforementioned trends.

In their paper Collective Classification of Textual Doc-

uments by Guided Self-Organization in T-Cell Cross-

regulation Dynamics, Abi-Haidar and Rocha analyse a

computational extension of the model of T-Cell cross-

regulation initially proposed by Carneiro et al. This work

provides several interesting contributions to the AIS liter-

ature. On the one hand, it elaborates the original, mini-

malist analytical model to incorporate heterogeneous

surface presentation and T-Cell specificities, which in turn,

provides insight into how the hypothesised mechanism

might unfold in a more realistic setting. On the other hand,

this demonstration is provided in the context of document

classification, where amino-acid sequences are replaced

with words and tolerance and immunity replaced with

skewed, two-class classification. The subsequent empirical

demonstration of the model’s performance in comparison

to established statistical algorithms provides a compelling

insight into the robustness of the biological model that

would be very difficult to assert using traditional means,

due to the lack of sufficient experimental data.

E. Hart (&)

Institute for Informatics and Digital Innovation,

Edinburgh Napier University, Edinburgh, UK

e-mail: E.Hart@napier.ac.uk

C. McEwan

I2D3 Integrative Immunology: Differentiation,

Diversity, Dynamics, UPMC Univ Paris 06, Paris, France

e-mail: chris.mcewan@upmc.fr

J. Timmis

Department of Computer Science and Department

of Electronics, University of York, York, UK

e-mail: jtimmis@cs.york.ac.uk

A. Hone

School of Mathematics, Statistics & Actuarial Science,

University of Kent, Kent, UK

e-mail: A.N.W.Hone@kent.ac.uk

123

Evol. Intel. (2011) 4:67–68

DOI 10.1007/s12065-011-0058-z

In Surrogate-assisted Clonal Selection Algorithms for

Expensive Optimization Problems, Bernardino et al. pro-

vide a thorough empirical assessment of using surrogate

functions in the CLONALG optimization algorithm. Sto-

chastic search algorithms, of which CLONALG is an

immune-inspired variant, often require a large amount of

function evaluations. Surrogate functions provide a light-

weight proxy objective function that can be used in place of

the true underlying objective function, which can be

exploited in several ways. Bernardino et al. show how

surrogate functions can be used to improve solutions given

the constraint of a fixed number of true objective evalua-

tions. Such a constraint is typical of inverse problems in

scientific computing, where the objective function may be

an expensive simulation. Their paper demonstrates the

applicability of these techniques to scale immune-inspired

optimisation algorithms to this important class of problems,

by comparison with a default CLONALG-based approach.

They also provide some empirical insight into the best

method of approximating the underlying objective function

by comparing nearest-neighbour and locally linear variants.

Finally, in On the Role of Age Diversity for Effective

Aging Operators, Jansen and Zarges perform a theoretical

study of the aging mechanisms in randomized search heu-

ristics. Aging is a procedure employed in both evolutionary

computation and AIS algorithms, whereby each search point

is assigned an age which increases with each generation, and

when a search point becomes older than some pre-defined

maximal age, it is deleted from the population. New search

points, or offspring, are then introduced, with the overall

effect being to increase the diversity of the population, but

there are various different strategies for assigning an age to

the offspring. This paper considers different aging operators

and provides asymptotic bounds on their performance when

applied to optimization of a particular example objective

function. These theoretical bounds are also compared with

experimental results for small problem sizes, revealing a gap

between theory and practice. The overall conclusion is that

not only diversity of search points, but also diversity with

respect to age plays an important role in determining the

performance of randomized search algorithms that employ

aging strategies.

68 Evol. Intel. (2011) 4:67–68

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